

import argparse
import gc
import json
import os
import re

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint


lowerCAmelCase_			      =      {
    '169M': 12,
    '430M': 24,
    '1B5': 24,
    '3B': 32,
    '7B': 32,
    '14B': 40,
}

lowerCAmelCase_			      =      {
    '169M': 7_68,
    '430M': 10_24,
    '1B5': 20_48,
    '3B': 25_60,
    '7B': 40_96,
    '14B': 51_20,
}


def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__			):
						snake_case_  =      list(state_dict.keys()			)
						for name in state_dict_keys:
												snake_case_  =      state_dict.pop(SCREAMING_SNAKE_CASE__			)
												# emb -> embedding
												if name.startswith('''emb.'''			):
																		snake_case_  =      name.replace('''emb.'''    ,						'''embeddings.'''			)
												# ln_0 -> pre_ln (only present at block 0)
												if name.startswith('''blocks.0.ln0'''			):
																		snake_case_  =      name.replace('''blocks.0.ln0'''    ,						'''blocks.0.pre_ln'''			)
												# att -> attention
												snake_case_  =      re.sub(R'''blocks\.(\d+)\.att'''    ,						R'''blocks.\1.attention'''    ,						SCREAMING_SNAKE_CASE__			)
												# ffn -> feed_forward
												snake_case_  =      re.sub(R'''blocks\.(\d+)\.ffn'''    ,						R'''blocks.\1.feed_forward'''    ,						SCREAMING_SNAKE_CASE__			)
												# time_mix_k -> time_mix_key and reshape
												if name.endswith('''.time_mix_k'''			):
																		snake_case_  =      name.replace('''.time_mix_k'''    ,						'''.time_mix_key'''			)
												# time_mix_v -> time_mix_value and reshape
												if name.endswith('''.time_mix_v'''			):
																		snake_case_  =      name.replace('''.time_mix_v'''    ,						'''.time_mix_value'''			)
												# time_mix_r -> time_mix_key and reshape
												if name.endswith('''.time_mix_r'''			):
																		snake_case_  =      name.replace('''.time_mix_r'''    ,						'''.time_mix_receptance'''			)

												if name != "head.weight":
																		snake_case_  =      """rwkv.""" + name

												snake_case_  =      weight
						return state_dict


def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=False    ,						SCREAMING_SNAKE_CASE__=None			):





						if tokenizer_file is None:
												print('''No `--tokenizer_file` provided, we will use the default tokenizer.'''			)
												snake_case_  =      50277
												snake_case_  =      AutoTokenizer.from_pretrained('''EleutherAI/gpt-neox-20b'''			)
						else:
												snake_case_  =      PreTrainedTokenizerFast(tokenizer_file=SCREAMING_SNAKE_CASE__			)
												snake_case_  =      len(SCREAMING_SNAKE_CASE__			)
						tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__			)

						# 2. Build the config
						snake_case_  =      list(NUM_HIDDEN_LAYERS_MAPPING.keys()			)
						if size is None:
												# Try to infer size from the checkpoint name
												for candidate in possible_sizes:
																		if candidate in checkpoint_file:
																								snake_case_  =      candidate
																								break
												if size is None:
																		raise ValueError('''Could not infer the size, please provide it with the `--size` argument.'''			)
						if size not in possible_sizes:
												raise ValueError(F'''`size` should be one of {possible_sizes}, got {size}.'''			)

						snake_case_  =      RwkvConfig(
						    vocab_size=SCREAMING_SNAKE_CASE__    ,						num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]    ,						hidden_size=HIDEN_SIZE_MAPPING[size]    ,						)
						config.save_pretrained(SCREAMING_SNAKE_CASE__			)

						# 3. Download model file then convert state_dict
						snake_case_  =      hf_hub_download(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			)
						snake_case_  =      torch.load(SCREAMING_SNAKE_CASE__    ,						map_location='''cpu'''			)
						snake_case_  =      convert_state_dict(SCREAMING_SNAKE_CASE__			)

						# 4. Split in shards and save
						snake_case_  =      shard_checkpoint(SCREAMING_SNAKE_CASE__			)
						for shard_file, shard in shards.items():
												torch.save(SCREAMING_SNAKE_CASE__    ,						os.path.join(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			)			)

						if index is not None:
												snake_case_  =      os.path.join(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			)
												# Save the index as well
												with open(SCREAMING_SNAKE_CASE__    ,						'''w'''    ,						encoding='''utf-8'''			) as f:
																		snake_case_  =      json.dumps(SCREAMING_SNAKE_CASE__    ,						indent=2    ,						sort_keys=SCREAMING_SNAKE_CASE__			) + """\n"""
																		f.write(SCREAMING_SNAKE_CASE__			)

												# 5. Clean up shards (for some reason the file PyTorch saves take the same space as the whole state_dict
												print(
												    '''Cleaning up shards. This may error with an OOM error, it this is the case don\'t worry you still have converted the model.'''			)
												snake_case_  =      list(shards.keys()			)

												del state_dict
												del shards
												gc.collect()

												for shard_file in shard_files:
																		snake_case_  =      torch.load(os.path.join(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			)			)
																		torch.save({k: v.cpu().clone() for k, v in state_dict.items()}    ,						os.path.join(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			)			)

						del state_dict
						gc.collect()

						if push_to_hub:
												if model_name is None:
																		raise ValueError('''Please provide a `model_name` to push the model to the Hub.'''			)
												snake_case_  =      AutoModelForCausalLM.from_pretrained(SCREAMING_SNAKE_CASE__			)
												model.push_to_hub(SCREAMING_SNAKE_CASE__    ,						max_shard_size='''2GB'''			)
												tokenizer.push_to_hub(SCREAMING_SNAKE_CASE__			)


if __name__ == "__main__":
					lowerCAmelCase_			      =      argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '''--repo_id''', default=None, type=str, required=True, help='''Repo ID from which to pull the checkpoint.'''
					)
					parser.add_argument(
					    '''--checkpoint_file''', default=None, type=str, required=True, help='''Name of the checkpoint file in the repo.'''
					)
					parser.add_argument(
					    '''--output_dir''', default=None, type=str, required=True, help='''Where to save the converted model.'''
					)
					parser.add_argument(
					    '''--tokenizer_file''',
					    default=None,
					    type=str,
					    help='''Path to the tokenizer file to use (if not provided, only the model is converted).''',
					)
					parser.add_argument(
					    '''--size''',
					    default=None,
					    type=str,
					    help='''Size of the model. Will be inferred from the `checkpoint_file` if not passed.''',
					)
					parser.add_argument(
					    '''--push_to_hub''',
					    action='''store_true''',
					    help='''Push to the Hub the converted model.''',
					)
					parser.add_argument(
					    '''--model_name''',
					    default=None,
					    type=str,
					    help='''Name of the pushed model on the Hub, including the username / organization.''',
					)

					lowerCAmelCase_			      =      parser.parse_args()
					convert_rmkv_checkpoint_to_hf_format(
					    args.repo_id,
					    args.checkpoint_file,
					    args.output_dir,
					    size=args.size,
					    tokenizer_file=args.tokenizer_file,
					    push_to_hub=args.push_to_hub,
					    model_name=args.model_name,
					)



'''simple docstring'''


import numpy as np





class        lowercase__    :





							'''simple docstring'''


							def __init__(       self    ):
											_SCREAMING_SNAKE_CASE					:			List[Any]							= (0, 0)
											_SCREAMING_SNAKE_CASE					:			List[str]							= None
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= 0
											_SCREAMING_SNAKE_CASE					:			List[str]							= 0
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= 0






							def __eq__(       self       ,   __snake_case    ):
											return self.position == cell.position






							def UpperCAmelCase_      (       self    ):
											print(self.position    )





class        lowercase__    :





							'''simple docstring'''


							def __init__(       self       ,   __snake_case=(5, 5)    ):
											_SCREAMING_SNAKE_CASE					:			str							= np.zeros(__snake_case    )
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= world_size[0]
											_SCREAMING_SNAKE_CASE					:			Tuple							= world_size[1]






							def UpperCAmelCase_      (       self    ):
											print(self.w    )






							def UpperCAmelCase_      (       self       ,   __snake_case    ):
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= [
											    (-1, -1),
											    (-1, 0),
											    (-1, 1),
											    (0, -1),
											    (0, 1),
											    (1, -1),
											    (1, 0),
											    (1, 1),
											]
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= cell.position[0]
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= cell.position[1]
											_SCREAMING_SNAKE_CASE					:			Tuple							= []
											for n in neughbour_cord:
															_SCREAMING_SNAKE_CASE					:			Optional[Any]							= current_x + n[0]
															_SCREAMING_SNAKE_CASE					:			List[Any]							= current_y + n[1]
															if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
																			_SCREAMING_SNAKE_CASE					:			Optional[int]							= Cell()
																			_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= (x, y)
																			_SCREAMING_SNAKE_CASE					:			Dict							= cell
																			neighbours.append(__snake_case    )
											return neighbours







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			Tuple							= []
				_SCREAMING_SNAKE_CASE					:			Optional[Any]							= []
				_open.append(SCREAMING_SNAKE_CASE__	)

				while _open:
								_SCREAMING_SNAKE_CASE					:			Any							= np.argmin([n.f for n in _open]	)
								_SCREAMING_SNAKE_CASE					:			List[Any]							= _open[min_f]
								_closed.append(_open.pop(SCREAMING_SNAKE_CASE__	)	)
								if current == goal:
												break
								for n in world.get_neigbours(SCREAMING_SNAKE_CASE__	):
												for c in _closed:
																if c == n:
																				continue
												_SCREAMING_SNAKE_CASE					:			Optional[int]							= current.g + 1
												_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			List[Any]							= n.position
												_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			List[str]							= goal.position
												_SCREAMING_SNAKE_CASE					:			Dict							= (ya - ya) ** 2 + (xa - xa) ** 2
												_SCREAMING_SNAKE_CASE					:			Any							= n.h + n.g

												for c in _open:
																if c == n and c.f < n.f:
																				continue
												_open.append(SCREAMING_SNAKE_CASE__	)
				_SCREAMING_SNAKE_CASE					:			Any							= []
				while current.parent is not None:
								path.append(current.position	)
								_SCREAMING_SNAKE_CASE					:			List[Any]							= current.parent
				path.append(current.position	)
				return path[::-1]


if __name__ == "__main__":
				UpperCAmelCase_						:  List[Any]				  =						Gridworld()
				# Start position and goal
				UpperCAmelCase_						:  Optional[Any]				  =						Cell()
				UpperCAmelCase_						:  Tuple				  =						(0, 0)
				UpperCAmelCase_						:  Optional[Any]				  =						Cell()
				UpperCAmelCase_						:  Tuple				  =						(4, 4)
				print(F"path from {start.position} to {goal.position}")
				UpperCAmelCase_						:  Union[str, Any]				  =						astar(world, start, goal)
				#   Just for visual reasons.
				for i in s:
								UpperCAmelCase_						:  Optional[int]				  =						1
				print(world.w)




